# Ranking and Selection with Simultaneous Input Data Collection

arXiv: https://arxiv.org/abs/2503.11773 · HTML: https://mlsanity.com/paper/2503.11773/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 48 references that are in the map.
- First posted on arXiv 14 Mar 2025.

## Builds on (most cited first, 5 of 5)

- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [The Empirical Likelihood Approach to Quantifying Uncertainty in Sample Average Approximation](https://mlsanity.com/paper/1604.02573/) (2016)
- [Subsampling to Enhance Efficiency in Input Uncertainty Quantification](https://mlsanity.com/paper/1811.04500/) (2018)
- [Distributionally Robust Selection of the Best](https://mlsanity.com/paper/1903.05828/) (2019)
- [Data-driven Ranking and Selection under Input Uncertainty](https://mlsanity.com/paper/1708.08526/) (2017)

## Abstract (arXiv)

In this paper, we propose a general and novel formulation of ranking and selection with the existence of streaming input data. The collection of multiple streams of such data may consume different types of resources, and hence can be conducted simultaneously. To utilize the streaming input data, we aggregate simulation outputs generated under heterogeneous input distributions over time to form a performance estimator. By characterizing the asymptotic behavior of the performance estimators, we formulate two optimization problems to optimally allocate budgets for collecting input data and running simulations. We then develop a multi-stage simultaneous budget allocation procedure and provide its statistical guarantees such as consistency and asymptotic normality. We conduct several numerical studies to demonstrate the competitive performance of the proposed procedure.